To diagnose diseases using low-field MRI devices, automatic image recognition techniques are required to detect anomalies in a given image. A critical component of this process is image segmentation, which involves dividing the image into coherent regions with similar features to detect the anomalies. Low quality images make segmentation difficult by the existence of noise in the images. The Chan-Vese model is a segmentation technique to segment images into two regions. This report aims to segment the low quality images obtained by the low-field MRI devices with use of the Chan-Vese model. This model is found to be particularly well-suited for handling the challenges posed by low-quality images which the low-field MRI devices produce. Future research should investigate if it is possible to further segment the region with the objects, such that each object has a separate region.